Advanced Predictive Modeling for Dam Occupancy Using Historical and Meteorological Data

Abstract

Dams significantly impact the environment, industries, residential areas, and agriculture. Efficient dam management can mitigate negative impacts and enhance benefits such as flood and drought reduction, energy efficiency, water access, and improved irrigation. This study tackles the critical issue of predicting dam occupancy levels precisely to contribute to sustainable water management by enabling efficient water allocation among sectors, proactive drought management, controlled flood risk mitigation, and preservation of downstream ecological integrity. Our research suggests that combining physical models of water inflow and outflow such as evapotranspiration using the Penman-Monteith equation, along with parameters like water consumption, solar radiation, and rainfall with data-driven models based on historical reservoir data is crucial for accurately predicting occupancy levels. We implemented various prediction models, including Random Forest, Extra Trees, Long Short-Term Memory, Orthogonal Matching Pursuit CV, and Lasso Lars CV. To strengthen our proposed model with robust evidence, we conducted statistical tests on the mean absolute percentage errors of the models. Consequently, we demonstrated the impact of physical model parameters on prediction performance and identified the best method for predicting dam occupancy levels by comparing it with findings from the scientific literature.